Block motion estimation method

ABSTRACT

A block motion estimation method for estimating a motion vector on the basis of a position of a frame block in a current picture compared to a position of said frame block in a reference picture, by determining, at a plurality of search points of said current picture, a variation of the current picture as compared to the reference picture, said search points defining a polygonal search area along the perimeter thereof, said search area including some reference search points and inner search points, the method comprising the following steps: sub-dividing a plurality of n-tuples each n-tuple comprising n of said search points, wherein n is any interger from 1 (normally 2, 3 or more); determining a selected n-tuple of said sub-plurality of n-tuples having the smallest sum of distortions; and comparing the distortion at said central reference search point and at least one inner search point adjacent to the selected n-tuple such as to determine that search point thereof having the smallest distortion which is used in the estimation process

BACKGROUND OF THE INVENTION

[0001] The invention relates to a block motion estimation method forestimating a motion vector on the basis of a position of a block in acurrent picture compared to a position of the found block in a referencepicture.

[0002] In the field of encoding a video sequence (video encoding), thecompression of video data has become a very important issue for reducingthe amount of data needed to be transmitted and/or stored for theencoding of a plurality of pictures in a quality which is sufficientlyhigh for a user.

[0003] A very important factor with respect to video data compression isthe motion estimation between subsequent pictures of the video sequence,which is used to extract motion information from the video sequence. Theextracted motion information is used for avoiding or at least reducingthe temproral redundancy in subsequent video pictures.

[0004] Block-matching motion estimation is widely applied in manymotion-compensated video coding techniques/standards such as ISOMPEG-1/2/4 and ITU-T H.261/262/263/263+/263L, which is aimed to exploitthe strong temporal redundancy between successive frames. Bypartitioning a current frame into non-overlapping rectangularblocks/macroblocks of equal size, a block matching method attempts tofind a block from a reference frame (past or future frame) that bestmatches a predefined block in the current frame. Matching is performedby minimizing a matching criterion, which in most cases is the meanabsolute error between this pair of blocks. The block in the referenceframe moves inside a search window centred around the position of theblock in the current frame. The best matched block producing the minimumdistortion is searched within the search window in the reference frame.The displacement of the current block with respect to the best matchedreference block in x and y directions composes the motion vectorassigned to this current block.

[0005] However, the motion estimation is quite computational intensiveand can consume up to 80% of the computational power of the encoder ifthe full search is used by exhaustively evaluating all possiblecandidate blocks within a predefined search window. Therefore, fastalgorithms are highly desired to significantly speed up the procedurewithout sacrificing the distortion sharply.

[0006] Many computationally efficient variants were developed, typicallyamong which are the so-called three-step search, the new three-stepsearch, the four-step search, the block-based gradient descent searchand the diamond search algorithms, compare for instance references [1],[2], [3], [4], [5].

[0007] In the block-based motion estimation, the search pattern withdifferent shapes or sizes has a great impact on the reachable searchspeed and the resulting distortion performance.

[0008] On one hand, in the three-step search, the new three-step search,the four-step search and the block-based gradient descent searchalgorithms, square-shaped search patterns of different sizes areemployed. These topics are described in [4] and [5].

[0009] On the other hand, the diamond search algorithm, as described in[2] and [3], adopts a diamond-shaped search pattern, which hasdemonstrated faster processing time with marginally worse distortion incomparison with the three-step search, the new three-step search and thefour-step search.

[0010] The search pattern used in the diamond search algorithm has arectangular, diamond shape. Two different sizes of diamonds areemployed. The larger one consists of nine search points (also denoted aschecking points), of which eight search points surround a central searchpoint. The small diamond search pattern consists of five inner searchpoints, of which four inner search points surround a central searchpoint to compose the diamond shape.

[0011] Recently, the inventors of the present invention have proposed ahexagon-based search algorithm (PCT/SG00/00176, unpublished). The basicidea of this concept can generally be seen in a hexagon-based searchalgorithm in the block motion estimation in a sequence of pictures, i.e.a video sequence, where the search algorithm can achieve significantspeed improvement over the diamond search algorithm with similardistortion performance. The hexagon-based search algorithm employs twodifferent sizes of hexagonal search patterns. The larger one consists ofseven search points, of which six search points surround a centralsearch point. The small hexagon search pattern comprises five innersearch points, of which four inner search points surround a centralsearch point.

[0012] However, there is still the emerging need to improve theprocessing speed for motion estimation.

SUMMARY OF THE INVENTION

[0013] It is an object of the present invention to provide a blockmotion estimation method which can be carried out with improvedprocessing speed compared to the above-described search algorithms ofthe related art.

[0014] The object is achieved by providing a block motion estimationmethod which increases the speed of the motion estimating process bychecking only a part instead of all inner search points within apolygonal search area without a significant loss of accuracy.

[0015] According to the block motion estimation method of the invention,a position of a frame block in a current picture as compared to aposition of said frame block in a reference picture is estimated bydetermining, at a plurality of search points of said reference picture,a variation of the current picture as compared to the reference picture.Said search points define a polygonal search area along the perimeter ofthe search area. Said search area includes some reference search pointschecked and inner search points to be checked. The method comprises thefollowing steps: The plurality of checked reference search points issub-divided into a sub-plurality of n-tuples, each n-tuple comprising nof said reference search points, wherein n is any integer number from 1,and is normally 2, 3 or more. For each n-tuple of said sub-plurality ofn-tuples, a distortion at each of said n reference search points of saideach n-tuple is already known from the previous step and the determineddistortions of said n search points is added so as to compute a sum ofdistortions of said each reference n-tuple. A selected n-tuple of saidsub-plurality of n-tuples is determined having the smallest sum ofdistortions among said sub-plurality of n-tuples. At least one closestinner search point within said search area is identified to be checked,each said at least one closest inner search point having a distance fromsaid selected n-tuple which is smaller than the distances of all theother further inner search points to be checked from said selectedn-tuple. Furthermore, the distortion at said at least one closest innersearch point is checked such as to determine that search point thereofhaving the smallest distortion.

[0016] It is mentioned that the comparison of the current minimumdistortion and the distortion of at least one inner search pointadjacent to the selected n-tuple is performed in order to determine thatsearch point thereof having the smallest distortion. This search pointwith the smallest distortion is used in the estimation process, in otherwords the position of this search point provides one with the positionto be estimated by the method. It is further noted that the searchpoints forming the n-tuples are usually different search points than theabove-mentioned inner search points.

[0017] The invention is based on the recognition that there exists astrong correlation between the tupel with the smallest sum of distortionand the adjacent inner search points.

[0018] The distortion can, for example, be determined based on the valueof a physical parameter of a picture element in the reference pictureand a picture element in the current picture, respectively. Such aphysical parameter assigned to the picture elements may be a luminanceinformation or a chrominance information, for instance. The calculationof the distortion can for example be performed by computing the meanabsolute error between a physical parameter of a picture element in thereference picture and the physical parameter of a picture element in thecurrent picture.

[0019] According to a preferred embodiment of the invention, thereference search points are the central search point in the search areaand the search points along the perimeter of the search area. However,especially in cases of large polygons or polygons with a low degree ofsymmetry, the centered reference search point can alternatively bedisplaced from the central portion of the polygonal search area. Comingback to the case where the centered reference search point is thecentral search point, before carrying out the method of the invention,preferably a coarse search has already been performed resulting in anorientation of the search area with respect to the reference picture inwhich the central search point has a smaller distortion than all theother search points located on the perimeter of the search region.

[0020] According to a preferred embodiment of the invention, this coarsesearch is performed using an algorithm from the related art, forinstance a three-step search method, a four-step search method, adiamond search method or a hexagonal search method.

[0021] The search can comprise a one-pixel search method or a half-pixelsearch method or a quarter-pixel search method. In other words: themethod of the invention is carried out preferably when the search isswitched from a coarse search, with the search area moving with respectto the picture, to a finer-resolution focused inner search within thesearch area. However, the combination of one of the coarse searchmethods with the block motion estimation method of the invention shallbe considered to be a part of the invention.

[0022] Preferably, the reference picture is a preceding or a followingpicture of the current picture. However, usually the reference pictureis a preceding picture.

[0023] The polygon defining the search area is a four-corner diamondaccording to a first preferred embodiment of the invention, while thepolygon is a hexagon according to a second preferred embodiment of theinvention. However, the shape of the search area is not restricted tothese two geometrical forms. The shape of the polygon can alternativelybe for instance a triangle, a rectangle, a pentagon, an octagon or thelike.

[0024] According to a preferred embodiment of the invention, the searchpoints of each n-tuple are adjacent search points in the search area.For example, three adjacent search points located on a face of afour-corner diamond can make up a 3-tuple, alternatively one searchpoint at one corner of the diamond and two search points located on twofaces adjacent to the corner can make up a 3-tuple. A 2-tuple can beformed by two adjacent search points located on a face of a hexagon.

[0025] However, the search points of an n-tuple need not necessarily beadjacent search points. Especially in cases of large polygons, it can bereasonable to chose the search points of an n-tuple separated from eachother by one or more further intermediate search points.

[0026] The number of search points is determined by the size of thepolygonal search area. Normally, the larger the polygonal search area,the larger is the number of search points. The lower level of the sizeof the polygonal search area is determined by the distance of adjacentsearch points.

DETAILED DESCRIPTION OF THE INVENTION BRIEF DESCRIPTION OF THE DRAWINGS

[0027]FIG. 1 shows a diagram illustrating a diamond search patternaccording to a preferred embodiment of the invention;

[0028]FIG. 2 shows a diagram illustrating a diamond search patternaccording to another preferred embodiment of the invention;

[0029]FIG. 3A shows a diagram illustrating a hexagonal search patternaccording to a further preferred embodiment of the invention;

[0030]FIG. 3B shows a diagram illustrating a hexagonal search patternaccording to the further preferred embodiment of the invention.

[0031] Many fast block motion estimation algorithms employ gradientmethods to find the optimal motion vector step by step. However, inthese algorithms, only the smallest distortion is identified while theother distortion information is not exploited for the followingnext-step search. In fact, there is a strong correlation among thesearch points to be checked in the following step and their neighbouringsearch points checked in the current step. To take advantage of all theavailable information maximally, a new efficient search scheme isproposed by the invention to minimize the number of search points to bechecked in the following search step. The distortion information of allthe checked points helps determine the “focused” region to be checked,i.e., a more restricted search in a smaller region in the followingsearch step results in the reduction of the number of search points. Thescheme is particularly useful when the search is switched from coarsesearch to focused inner search. The invented scheme can be incorporatedinto any known fast algorithm such as three-step search, four-stepsearch, diamond search and hexagonal search to further improve thesealgorithms significantly. Especially the half- or quarter-pixel searchcan be benefited greatly from the new scheme by evaluating only fewerthan half of the search points that are required regularly.

[0032] Fast block motion estimation algorithms find motion vectors stepby step. It is noted that for these fast algorithms only the point withsmallest distortion is utilized while the other distortion informationof the other checked points is not been exploited for the followingsearch. In fact, there is a strong correlation among the search pointsto be checked in the following step and their neighbouring search pointschecked in the current step. In particular, when performingfiner-resolution inner search, strong correlation exists among the innersearch points to be checked in the shrunk pattern (such as diamond orsquare search pattern) and their surrounding search points checked inthe large pattern. To fulfil more efficient search, the redundancy canbe exploited for further speed improvement. In the scheme disclosedhere, apart from finding the search point with the current minimumdistortion, one also needs to consider the distortions of the otherpoints, and one selects only those inner search points for furtheranalysis that are most likely to yield a smaller distortion in the nextstep. In contrast to the fast algorithms of the related art, the searchscheme of the invention maximally utilizes the distortion information ofall checked points to minimize the number of search points. Thedistortion information of the currently checked points is fullyexploited to make a more restricted search in the following step. Thusthe number of search points can be reduced by only checking those thatare most likely to be better matched search points. It is assumed thatthe global minimum has a monotonic distortion, and the nearer a searchpoint is to the global minimum, the smaller is the distortion of thissearch point. Based on this reasonable assumption, one only needs tocheck the portion of the search points in the following step that arenearer to the checked points with relatively smaller distortions. Forexample, as a large search pattern is switched to its shrunk one in manyfast methods such as the three-step search method and its variants, thefour-step search method, the diamond search method and the hexagonalsearch method, the focused inner search can be performed by onlyevaluating a portion of new search points that are nearer to theevaluated search points with smaller distortions rather than by carryingout the complete inner search. This can save a lot of search pointsespecially for the focused inner search or half- or quarter-pixelsearch. Based on the combination of this search points-saving scheme andthe hexagonal search method, a hexagonal adaptive search technique isdisclosed according to an preferred embodiment of the invention. Thehexagonal adaptive search technique also exploits the motion vectors ofneighbouring blocks to further speed up the search process.

[0033] The invention can generally be seen in an improved search schemefor carrying out a finer-resolution search for the position of a frameblock in a reference picture compared to the position of the frame blockin the current picture. This implies that before carrying out the methodof the invention, usually (but not necessarily) a coarse search hadalready been carried out. As a result of this preceding coarse search,the search area is usually located in the reference picture in a waythat the distortion at the central search point is smaller than thedistortions at all search points along the perimeter of the search area.In other words, it is the main job of the block motion estimation methodof the invention to find out the particular one of the inner searchpoints which may have the smallest distortion among all search points.Concerning this finer-resolution search, the method of the inventionusually analysed fewer inner search points than the finer-resolutionsearch algorithms according to the related art. Thus, the motionestimation may be processed with improved speed compared to the motionestimation using the algorithms of the related art.

[0034] In summary, the block motion estimation method according to theinvention may find any point in the motion field with fewer analysedsearch points than the algorithms of the related art.

[0035] The block motion estimation method will be described in detailwith reference to preferred embodiments of the invention exemplified inthe accompanying drawings.

[0036] Referring to FIG. 1, a preferred embodiment of the block motionestimation method of the invention is described for the case of adiamond search pattern.

[0037] According to this embodiment, the block motion estimation methodfor estimating a position of a frame block in a reference picture 100 ascompared to a position of the frame block in the current picture isperformed by determining, at a plurality of search points 101 of thecurrent picture 100, a variation of the reference picture 100 ascompared to the current picture, the search points 101 defining afour-corner diamond-shaped search area 102 along the perimeter thereof,the search area 102 including a central search point 103 and innersearch points 104. The method comprises the following steps:sub-dividing the plurality of search points 101 into a sub-plurality of3-tuples 105, each 3-tuple 105 comprising three of the search points101; for each 3-tuple 105 of the sub-plurality of 3-tuples 105,determining a distortion at each of the three search points 101 of each3-tuple 105 and adding the determined distortions of the three searchpoints 101 so as to compute a sum of distortions of each 3-tuple 105;determining a selected 3-tuple 106 of the sub-plurality of 3-tuples 105having the smallest sum of distortions among the sub-plurality of3-tuples 105; identifying two closest inner search points 107 within thesearch area 102, the two closest inner search points 107 having adistance from the selected 3-tuple 105 which is smaller than thedistances of two further inner search points 108 from the selected3-tuple 106; comparing the distortion at the two closest inner searchpoints 107 and the central search point 103 such as to determine thatsearch point 107, 103 thereof having the smallest distortion.

[0038] According to the embodiment of the invention described withreference to FIG. 1, the reference search point 103 is a central searchpoint in the search area 102, i.e. the reference search point 103 isbasically located in the centre of the search area 102. The number ofsearch points 101 making up a 3-tuple 105 is three according to thedescribed embodiment (n=3) so that the 3-tuples 105 can be denoted astriples. The three search points 101 of each 3-tuple 105 are adjacentsearch points 101 in the search area 102. Strictly speaking, the searchpoints 101 forming the 3-tuples 105 are located on the perimeter of thesearch area 102. As shown in FIG. 1, the polygon that equals to thesearch area 102 is a four-corner diamond having four faces, wherein eachface makes up one 3-tuple 105, wherein each of the four 3-tuples 105 isformed by three search points 101 located on one of the faces of thefour-corner diamond.

[0039] In a method step that is usually preceding the method steps ofthe block motion estimation method of the invention, the above-describeddiamond search method is carried out with the result that the distortionat the central search point 103 is smaller than the distortions at theeight search points 101 located on the perimeter of the diamond-shapedsearch area 102. This scenario is the starting point of the block motionestimation method of the invention. In other words: the complete diamondsearch algorithm of the related art usually employs two different sizesof diamonds, a larger one for a coarse search and a smaller one for afiner-resolution search (see description above). However, when combiningthe diamond search algorithm of the related art with the describedembodiment of the block motion estimation method of the invention, onlythe coarse search with the larger diamond is carried out according tothe related art, whereas the finer-resolution search with the smallerdiamond is substituted by the block motion estimation method of theinvention. This means that it is a goal of the block motion estimationmethod of the invention to estimate a particular one of the inner searchpoints 104 and of the central search point 103 at which the distortionis minimal. The position of this particular search point 104, 103 equalsto the position of the frame block in the reference picture 100 to beestimated.

[0040] In the following, it is described how to carry out the innersearch in the case of the diamond-shaped search area 102 using thesearch scheme of the invention. The inner search within the small searcharea 102 determines the final motion vector. The search scheme of theinvention leads to check only a portion of the inner search points 104,namely the two closest inner search points 107, that are near to thechecked search points 101 of the selected 3-tuple 106 with the smallestsum of distortions. This can save around half or more search points forthe focused inner search. It is assumed that the distortion at each ofthe search points 101, 103, 104 in the diamond-shaped search area 102 isknown or at least determinable. In the following, the preferredembodiment of the efficient inner search scheme of the invention isdescribed basing on exploiting the information of the eight distortionsof the search points 101.

[0041] Since the diamond-shaped search area 102 has four faces, theconsideration of three adjacent search points 101 along each face willproduce two closest inner search points 107 to be checked (compare FIG.1). By adding the distortion values of each triple 105 of three adjacentsearch points 101 along each face in the large diamond, one candetermine the face corresponding to the smallest sum of the distortionswhich is denoted as selected 3-tuple 106. Then one simply needs to checkthe two closest inner search points 107 near to the selected 3-tuple 106rather than all the four inner search points 104 (see FIG. 1). Thedistortions at the two closest inner search points 107 can be comparedwith the distortion of the central search point 103 to estimate thesearch point 103, 107 with the smallest distortion among all searchpoints of the picture 100 (or at least of a selected portion of thepicture 100).

[0042] Referring now to FIG. 2, the block motion estimation methodaccording to another preferred embodiment of the invention is describedusing a diamond search pattern.

[0043] According to this other embodiment, the block motion estimationmethod for estimating a position of a frame block in a reference picture200 as compared to a position of the frame block in the current pictureis performed by determining, at a plurality of search points 201 of thecurrent picture 200, a variation of the current picture 200 as comparedto the reference picture, the search points 201 defining a four-cornerdiamond-shaped search area 202 along the perimeter of the search area202, the search area 202 including a central search point 203 and innersearch points 204. The method comprises the following steps:sub-dividing the plurality of search points 201 into a sub-plurality of3-tuples (not shown in FIG. 2), each 3-tuple comprising three of thesearch points 201; for each 3-tuple of the sub-plurality of 3-tuples,determining a distortion at each of the three search points 201 of each3-tuple and adding the determined distortions of the three search points201 so as to compute a sum of distortions of each 3-tuple; determining aselected 3-tuple 205 of the sub-plurality of 3-tuples having thesmallest sum of distortions among the sub-plurality of 3-tuples;identifying at least one closest inner search point 206 within thesearch area 202, the closest inner search point 206 having a distancefrom the selected 3-tuple 205 which is smaller than the distances offurther inner search points 207 from the selected 3-tuple 205; comparingthe distortion at the at least one closest inner search point 206 andthe central search point 203 such as to determine that search point 206,203 thereof having the smallest distortion.

[0044] The block motion estimation method according to the embodimentdescribed with reference to FIG. 2 differs only in a few aspects fromthe embodiment described above referring to FIG. 1. Therefore, in thefollowing, it will be pointed out to these differences, whereas thenon-mentioned aspects are basically identical for both the describedembodiments.

[0045] As shown in FIG. 2, the polygon that equals to the search area202 is a four-corner diamond having four faces and four corners. Eachface makes up one 3-tuple (not shown in FIG. 2) in the way describedabove with reference to FIG. 1, wherein each of the four 3-tuples isformed by three search points 201 located on one of the faces of thediamond. Beyond this, four further 3-tuples are formed by one searchpoint 201 at one corner of the diamond and by two search points 201located on two different faces adjacent to this corner. This means thataltogether eight 3-tuples of search points 201 are formed according tothe embodiment of the block motion estimation method of the inventiondescribed referring to FIG. 2. As one can gather from FIG. 2, theselected 3-tuple 205 is formed by one search point 201 at one corner ofthe diamond and by two search points 201 located on two faces adjacentto this corner.

[0046] Again, it is a main goal of the block motion estimation method ofthe invention to substitute and to improve the finer search within thesmaller diamond, whereas the preceding coarse search with the largerdiamond is carried out as in the related art.

[0047] According to the present embodiment, the edges or sidesinformation concerning the distortion in the large diamond is exploitedby adding the distortion values of any three adjacent search points 201in the large diamond search pattern. Here, one totally has eight3-tuples (triples) in the large diamond pattern. The triple that has thelowest total distortion is chosen as the selected 3-tuple 205 for thefinal inner search. The number of inner search points 204 to be analysedin the focused inner search is either one or two depending on theposition of the triple 205 with the lowest total distortion. FIG. 2illustrates the inner search in the frame for the case of the so-calledVertex Search. Vertex Search means that one of the four 3-tuplescomprising search points 201 from two different faces of thediamond-shaped search area 202 has the smallest sum of distortions. Inthis Vertex Search, only one closest inner search point 206 will bechecked which is located in between the centre search point 203 of thelarge diamond 202 and the selected 3-tuple 205 (compare FIG. 2). Incontrast to this, in the frame of the so-called Face Search, the twoinner search points nearest to the triple face with the smallest sum ofdistortions are checked. In short, the Face Search corresponds to thescenario described above referring to FIG. 1. According to the otherembodiment of the block motion estimation method of the inventiondescribed here, the average number of inner search points 204 to bechecked is beneficially further reduced compared to the number of innersearch points 104 to be checked according to the first describedembodiment.

[0048] With reference to FIG. 3A and FIG. 3B, a further preferredembodiment of the block motion estimation method of the invention isdescribed in detail.

[0049] According to this further embodiment, the block motion estimationmethod for estimating a position of a frame block in a reference picture300 as compared to a position of the frame block in the current pictureis performed by determining, at a plurality of search points 301 of thecurrent picture 300, a variation of the current picture 300 as comparedto the reference picture, the search points 301 defining a hexagonalsearch area 302 along the perimeter thereof, the search area 302including a central search point 303 and inner search points 304. Themethod comprises the following steps: sub-dividing the plurality ofsearch points 301 into a sub-plurality of 2-tuples 305, each 2-tuple 305comprising two of the search points 301; for each 2-tuple 305 of thesub-plurality of 2-tuples 305, determining a distortion at both searchpoints 301 of each 2-tuple 305 and adding the determined distortions ofthe two search points 301 so as to compute a sum of distortions of each2-tuple 305; determining a selected 2-tuple 306 of the sub-plurality of2-tuples 305 having the smallest sum of distortions among thesub-plurality of 2-tuples 305; identifying at least one closest innersearch point 307 within the search area 302, the closest inner searchpoint 307 having a distance from the selected 2-tuple 306 which issmaller than the distances of further inner search points 308 from theselected 2-tuple 306; comparing the distortion at the at least oneclosest inner search point 307 and the central search point 303 such asto determine that search point 307, 303 thereof having the smallestdistortion.

[0050] It is emphasized that the comparison of distortions in the finalmethod step with the distortion at the central (reference) search point303 is in some sense optional, as the distortion at the central searchpoint 303 has usually already been analysed in the usually precedingcoarse search with the large diamond.

[0051] According to the embodiment of the method of the inventiondescribed with reference to FIG. 3A, the reference search point 303 isagain a central search point in the search area 302, i.e. the referencesearch point 303 is basically located in the centre of thehexagon-shaped search area 302. The number of search points 301 makingup a 2-tuple 305 is two according to the described embodiment (n=2). Thetwo search points 301 of each 2-tuple 305 are adjacent search points 301in the search area 302. Strictly speaking, the search points 301 formingthe 2-tuples 305 are located on the perimeter of the search area 302. Asshown in FIG. 3A, the polygon that equals to the search area 302 is ahexagon having six faces, each face forming one 2-tuple 305, whereineach of the six 2-tuples 305 formed by two search points 301 is locatedon one of the six faces of the hexagon. Altogether, six search points301 are located on the six corners of the hexagon-shaped search area302, and the central search point 303 is surrounded by the six searchpoints 301 and is located basically in the centre of gravity of thehexagon.

[0052] In the following, an efficient method taking advantage of theknowledge of the distortion information of all the six checked searchpoints 301 on the corners of the hexagon shown in FIG. 3A is described.According to the described edge-based inner search method, firstly thesum of distortions for each pair of two search points 301 in each face(i.e. for all the six 2-tuples 305) of the large hexagonal searchpattern (i.e. of the search area 302) is calculated. Since the largehexagon has six faces, there are six sums of distortion values obtainedfrom adding the distortion values of two search points 301 (also denotedas check points) along each of the six edges to be compared. The 2-tuple305 of the large hexagonal search pattern that results in the smallestdistortion sum value is used as the selected 2-tuple 306 for focusedinner search. Only the closest inner search points 307 nearest to theselected 2-tuple 306 and being located within the hexagonal pattern needto be evaluated for the inner search. Referring to FIG. 3A, threeclosest inner search points 307 are used in the focused inner search,whereas the five further inner search points 308 need not be evaluatedfor the inner search. According to the scenario illustrated in FIG. 3A,three closest inner search points 307 have to be checked, as thesmallest distortion sum occurs in one of the two horizontally oriented2-tuples 305, namely the lower horizontal 2-tuple 305 of FIG. 3A is theselected 2-tuple 306. In contrast to this, only two closest inner searchpoints 307 a have to be checked, if the smallest distortion sum occursin one of the four remaining slanting 2-tuples 305 of the hexagon 302.Such a situation is shown in FIG. 3B. Furthermore, one has to mentionthat the selected 2-tuple is a different one in FIG. 3B (the lower leftslanted 2-tuple 305, reference sign 306 a) than in FIG. 3A (the lowerhorizontal 2-tuple 305, reference sign 306).

[0053] Combining the inner search scheme, i.e. the block motionestimation method explained above, and the large hexagonal searchpattern of the related art, a more efficient block motion estimationalgorithm being part of the invention is obtained, which also exploitsthe motion information in the region of support comprising theneighbouring macroblocks. Two different search patterns are employedaccording to different motion activities. As the motion activity of theneighbour macroblocks is not very high or the neighbouring motionvectors can provide a good initial motion vector, the block motionestimation algorithm will adopt the small hexagon (diamond or cross)search pattern for the gradient search. Otherwise, i.e., in the case ofmotion activity is high and the neighbouring macroblocks can not providegood initial motion vector, the block motion estimation algorithm willperform the search using the hexagonal search combining the largehexagonal search pattern and the efficient inner search described above.

[0054] Summarizing, from the above description, one can see that theproposed efficient search schemes attempt to maximally exploit thestrong correlation among the inner search points to be checked in theshrunk pattern (e.g. diamond or hexagon) and their surrounding searchpoints checked in the large pattern. The same idea can be applied toother search patterns, e.g., square pattern. Several methods (comparereferences [1-5]) have been suggested and some other variants can bealso designed based on the same idea. Using the distortion informationin a group, such as the distortion information of a tuple comprising atleast two search points, instead of using distortion information at eachindividual point, one can achieve more robust performance. Combining theefficient inner search with the hexagonal search method, a more powerfulsearch algorithm is developed, which also exploits the motioninformation in the region of support comprising the neighbouringmacroblocks. The efficient inner search method can be easilyincorporated into some other fast motion estimation algorithms such asthe three-step search method and its variants, four-step search method,etc.

[0055] The invention may be implemented using a special electroniccircuit, i.e. in hardware, or using a computer program, i.e. insoftware.

[0056] The block motion estimation method is preferably used in thefield of video encoding.

[0057] The following publications are cited in this document:

[0058] [1] K. Ma and P. Hosur, Performance Report of Motion Vector FieldAdaptive Search Technique (MVFAST), ISO/IEC JTC1/SC29/WG11 N3325, March2000.

[0059] [2] S. Zhu and K. Ma, “A new diamond search algorithm for fastblock-matching motion estimation,” IEEE Transactions on ImageProcessing, vol. 9, no. 2, pp. 287-290, 2000.

[0060] [3] J. Tham, S. Ranganath, M. Ranganath and A. Kassim, “A novelunrestricted centre-biased diamond search algorithm for block motionestimation”, IEEE Transaction on Circuits & Systems for VideoTechnology, vol. 8, no. 4, pp. 369-377, 1998.

[0061] [4] R. Li, B. Zeng and M. L. Liou, “A new three step searchalgorithm for block motion estimation”, IEEE Transactions on Circuits &Systems for Video Technology, vol. 4, pp. 438-442, 1994.

[0062] [5] L. M. Po and W. C. Ma, “A novel four-step search algorithmfor fast block motion estimation”, IEEE Transactions on Circuits &Systems for Video Technology, vol. 6, pp. 313-317, 1996.

[0063] List of Reference Signs

[0064]100 reference picture

[0065]101 search points

[0066]102 search area

[0067]103 central search point

[0068]104 inner search points

[0069]105 3-tuple

[0070]106 selected 3-tuple

[0071]107 closest inner search point

[0072]108 further inner search point

[0073]200 reference picture

[0074]201 search points

[0075]202 search area

[0076]203 central search point

[0077]204 inner search points

[0078]205 selected 3-tuple

[0079]206 closest inner search point

[0080]207 further inner search point

[0081]300 reference picture

[0082]301 search points

[0083]302 search area

[0084]303 central search point

[0085]304 inner search points

[0086]305 2-tuple

[0087]306 selected 2-tuple

[0088]307 closest inner search point

[0089]307 a closest inner search point

[0090]308 further inner search point

What is claimed is:
 1. A block motion estimation method for estimating amotion vector on the basis of a position of a frame block in a currentpicture compared to a position of said frame block in a referencepicture by determining, at a plurality of search points of said currentpicture, a variation of the current picture as compared to the referencepicture, said search points defining a polygonal search area along theperimeter thereof, said search area including some reference searchpoints checked and inner search points to be checked, the methodcomprising: sub-dividing the plurality of checked search points into asub-plurality of n-tuples, each n-tuple comprising n of said searchpoints, wherein n is any interger number from 1, for each n-tuple ofsaid sub-plurality of n-tuples, determining a distortion at each of saidn search points of said each n-tuple and adding the determineddistortions of said n search points so as to compute a sum ofdistortions of said each n-tuple, determining a selected n-tuple of saidsub-plurality of n-tuples having the smallest sum of distortions amongsaid sub-plurality of n-tuples, identifying at least one closest innersearch point within said search area, said closest inner search pointhaving a distance from said selected n-tuple which is smaller than thedistances of further inner search points from said selected n-tuple,comparing the distortion at said at least one closest inner search pointand said reference search point such as to determine that search pointthereof having the smallest distortion.
 2. The method according to claim1, wherein one of said reference search points is a central search pointin said search area.
 3. The method according to claim 1, wherein thesearch points of each said n-tuple are the search points along theperimeter of said search area.
 4. The method according to claim 1,wherein the reference picture is a preceding or a following picture ofthe current picture.
 5. The method according to claim 1, wherein n isequal to two.
 6. The method according to claim 1, wherein n is equal tothree.
 7. The method according to claim 1, which comprises a three-stepsearch method, a four-step search method, a diamond search method or ahexagonal search method.
 8. The method according to claim 7, whichcomprises a one-pixel search method or a half-pixel search method or aquarter-pixel search method.
 9. The method according to claim 2, whereinn is equal to three, wherein the polygon is a four-corner diamond havingfour faces, wherein said sub-plurality of n-tuples comprises four3-tuples, wherein each of said 3-tuples is formed by three search pointslocated on one of said faces.
 10. The method according to claim 9,wherein the distortion of two closest inner search points are comparedwith the distortion of the central search point.
 11. The methodaccording to claim 2, wherein n is equal to three, wherein the polygonis a four-corner diamond having four faces and four corners, whereinsaid sub-plurality of n-tuples comprises eight 3-tuples, wherein four ofsaid eight 3-tuples are formed by three search points located on one ofsaid faces and four of said eight 3-tuples are formed by one searchpoint at one of said four corners and by two of said search pointslocated on two of said faces adjacent to said one of said four corners.12. The method according to claim 11, wherein the distortion of one ortwo closest inner search points are compared with the distortion of thecentral search point.
 13. The method according to claim 2, wherein n isequal to two, wherein the polygon is a hexagon having six faces, whereinsaid sub-plurality of n-tuples comprises six 2-tuples, wherein each ofsaid 2-tuples is formed by two search points located on one of saidfaces.
 14. The method according to claim 13, wherein the distortion oftwo or three closest inner search points are compared with thedistortion of the central search point.